1 Processed data

This is an interactive table of the covariate data.

2 Normalisation quality control metrics

2.1 Principal component analysis

The principal component analysis plot shown below was generated using the most varying 500 genes across all samples.

The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design.

In presence of strong biological signal, the samples should cluster with the biological condition. When samples are clustered according to other effects (for example patient, or technical batch), great care must be used when interpreting the results, as the other effects will considerably reduce the ability to extract meaningful biological information.

## pdf 
##   2
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## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

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2.2 Hierarchical clustering

The hierarchical clustering shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

The hierarchical clustering can provide clues on which groups of genes could affect the clustering of samples.

Download plot

2.3 Sample similarity

The hierarchical clustering shown below was generated using all the full normalised dataset (15979 genes). The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

Download plot

2.4 Normalised expression densities

The expression values are obtained by the “vst” method, where the experimental design has been used for normalisation.

Download plot

2.5 DESeq2 normalisation

Download plot

2.6 Cox outliers

Download plot

3 Volcano plots for all contrasts

4 Contrasts

Contrasts generated by the pipeline.

4.1 other

4.1.1 MA plot

A MA plot of the contrast other.

4.1.2 Results table

An interactive data table of the contrast results for other. Only results with adjusted p value smaller than 0.1 are included (total 7071 results shown).

## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
## DT/server.html

4.1.3 tmod enrichment analysis for other

Table. Summary of the results for contrast other shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
tmod 44 32 25 17
msigdb_reactome 15 12 10 6
msigdb_hallmark 3 3 3 3
msigdb_kegg 10 8 6 6
msigdb_mir 0 0 0 0
msigdb_go_bp 123 74 47 25

Table. Results of the tmod enrichment analysis for contrast other. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

4.1.3.0.1
4.1.3.0.1.1 tmod.pval
4.1.3.0.1.2 msigdb_reactome.pval
4.1.3.0.1.3 msigdb_hallmark.pval
4.1.3.0.1.4 msigdb_kegg.pval
4.1.3.0.1.5 msigdb_mir.pval

No results at the specified thresholds

4.1.3.0.1.6 msigdb_go_bp.pval

Fig. Upset plot.

4.1.3.0.2
4.1.3.0.2.1 tmod.pval

4.1.3.0.2.2 msigdb_reactome.pval

4.1.3.0.2.3 msigdb_hallmark.pval

4.1.3.0.2.4 msigdb_kegg.pval

4.1.3.0.2.5 msigdb_mir.pval


Too few results to generate upset plot.

4.1.3.0.2.6 msigdb_go_bp.pval

4.1.4 cluster profiler results

4.1.4.1 Dot plot

Dot plot for cluster profiler results for contrast other.

MSigDb
1

2

GO
1

2

KEGG
1

4.1.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast other.

MSigDb
1

2

GO
1

2

KEGG
1

4.1.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast other.

MSigDb
1

2

GO
1

2

KEGG
1

4.2 SC2

4.2.1 MA plot

A MA plot of the contrast SC2.

4.2.2 Results table

An interactive data table of the contrast results for SC2. Only results with adjusted p value smaller than 0.1 are included (total 4011 results shown).

4.2.3 tmod enrichment analysis for SC2

Table. Summary of the results for contrast SC2 shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
tmod 69 38 31 21
msigdb_reactome 68 55 46 30
msigdb_hallmark 6 6 6 6
msigdb_kegg 12 11 11 2
msigdb_mir 0 0 0 0
msigdb_go_bp 165 96 62 36

Table. Results of the tmod enrichment analysis for contrast SC2. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

4.2.3.0.1
4.2.3.0.1.1 tmod.pval
4.2.3.0.1.2 msigdb_reactome.pval
4.2.3.0.1.3 msigdb_hallmark.pval
4.2.3.0.1.4 msigdb_kegg.pval
4.2.3.0.1.5 msigdb_mir.pval

No results at the specified thresholds

4.2.3.0.1.6 msigdb_go_bp.pval

Fig. Upset plot.

4.2.3.0.2
4.2.3.0.2.1 tmod.pval

4.2.3.0.2.2 msigdb_reactome.pval

4.2.3.0.2.3 msigdb_hallmark.pval

4.2.3.0.2.4 msigdb_kegg.pval

4.2.3.0.2.5 msigdb_mir.pval


Too few results to generate upset plot.

4.2.3.0.2.6 msigdb_go_bp.pval

4.2.4 cluster profiler results

4.2.4.1 Dot plot

Dot plot for cluster profiler results for contrast SC2.

MSigDb
1

2

GO
1

2

KEGG
1

4.2.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast SC2.

MSigDb
1

2

GO
1

2

KEGG
1

4.2.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast SC2.

MSigDb
1

2

GO
1

2

KEGG
1

4.3 SC2_vs_other

4.3.1 MA plot

A MA plot of the contrast SC2_vs_other.

4.3.2 Results table

An interactive data table of the contrast results for SC2_vs_other. Only results with adjusted p value smaller than 0.1 are included (total 8473 results shown).

## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
## DT/server.html

4.3.3 tmod enrichment analysis for SC2_vs_other

Table. Summary of the results for contrast SC2_vs_other shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
tmod 55 38 23 14
msigdb_reactome 11 4 4 3
msigdb_hallmark 3 3 3 3
msigdb_kegg 1 1 0 0
msigdb_mir 0 0 0 0
msigdb_go_bp 105 47 29 12

Table. Results of the tmod enrichment analysis for contrast SC2_vs_other. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

4.3.3.0.1
4.3.3.0.1.1 tmod.pval
4.3.3.0.1.2 msigdb_reactome.pval
4.3.3.0.1.3 msigdb_hallmark.pval
4.3.3.0.1.4 msigdb_kegg.pval
4.3.3.0.1.5 msigdb_mir.pval

No results at the specified thresholds

4.3.3.0.1.6 msigdb_go_bp.pval

Fig. Upset plot.

4.3.3.0.2
4.3.3.0.2.1 tmod.pval

4.3.3.0.2.2 msigdb_reactome.pval

4.3.3.0.2.3 msigdb_hallmark.pval

4.3.3.0.2.4 msigdb_kegg.pval

4.3.3.0.2.5 msigdb_mir.pval


Too few results to generate upset plot.

4.3.3.0.2.6 msigdb_go_bp.pval

4.3.4 cluster profiler results

4.3.4.1 Dot plot

Dot plot for cluster profiler results for contrast SC2_vs_other.

MSigDb
1

2

GO
1

2

KEGG
1

4.3.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast SC2_vs_other.

MSigDb
1

2

GO
1

2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

KEGG
1

4.3.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast SC2_vs_other.

MSigDb
1

2

GO
1

2

Error in [.data.frame(d, , 2) : undefined columns selected

KEGG
1

5 Functional analysis

5.1 Gene set enrichment analysis with tmod

5.1.1 Overview

Table. Overview of the databases for which gene set enrichment using tmod was performed.

ID Name Description TaxonID N
tmod Co-expression gene sets (tmod) Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information. 9606 606
msigdb_reactome Reactome gene sets (MSigDB) Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 1532
msigdb_hallmark Hallmark gene sets (MSigDB) Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 50
msigdb_kegg KEGG pathways (MSigDB) KEGG pathways from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 186
msigdb_mir MIR targets (MSigDB) MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 2377
msigdb_go_bp GO Biological Process (MSigDB) GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 7530

5.2 tmod enrichment analysis results for database Co-expression gene sets (tmod).

5.2.1 Summary

Database ID: tmod.

Description: Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
other_ID0.pval 59 48 33 22
SC2_ID1.pval 113 82 44 30
SC2_vs_other_ID2.pval 106 70 46 23 
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.2.2 Figure

Fig. Panel plot showing results for the database tmod.

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.2.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.3 tmod enrichment analysis results for database Reactome gene sets (MSigDB).

5.3.1 Summary

Database ID: msigdb_reactome.

Description: Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
other_ID0.pval 49 31 20 12
SC2_ID1.pval 228 182 148 77
SC2_vs_other_ID2.pval 68 40 17 9 
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.3.2 Figure

Fig. Panel plot showing results for the database msigdb_reactome.

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.3.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.4 tmod enrichment analysis results for database Hallmark gene sets (MSigDB).

5.4.1 Summary

Database ID: msigdb_hallmark.

Description: Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
other_ID0.pval 14 12 10 8
SC2_ID1.pval 21 13 13 11
SC2_vs_other_ID2.pval 24 19 13 11

5.4.2 Figure

Fig. Panel plot showing results for the database msigdb_hallmark.

5.4.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.5 tmod enrichment analysis results for database KEGG pathways (MSigDB).

5.5.1 Summary

Database ID: msigdb_kegg.

Description: KEGG pathways from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
other_ID0.pval 24 22 15 8
SC2_ID1.pval 30 20 16 9
SC2_vs_other_ID2.pval 19 7 6 2

5.5.2 Figure

Fig. Panel plot showing results for the database msigdb_kegg.

5.5.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.6 tmod enrichment analysis results for database MIR targets (MSigDB).

5.6.1 Summary

Database ID: msigdb_mir.

Description: MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
other_ID0.pval 0 0 0 0
SC2_ID1.pval 0 0 0 0
SC2_vs_other_ID2.pval 0 0 0 0

No figure produced because there were less than 2 significant results enrichment results.

5.7 tmod enrichment analysis results for database GO Biological Process (MSigDB).

5.7.1 Summary

Database ID: msigdb_go_bp.

Description: GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
other_ID0.pval 563 367 220 118
SC2_ID1.pval 885 599 383 193
SC2_vs_other_ID2.pval 766 465 275 125 
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.7.2 Figure

Fig. Panel plot showing results for the database msigdb_go_bp.

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.7.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.8 Cluster profiler summary overview by database

5.8.1 Overview

Table. Overview of the databases for which gene set enrichment using cluster_profiler was performed.

5.8.2 MSigDb.H

Fig. Panel plot showing results for the database MSigDb.H. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.

5.8.3 MSigDb.C2

Fig. Panel plot showing results for the database MSigDb.C2. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.

5.8.4 GO.BP

Fig. Panel plot showing results for the database GO.BP. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

5.8.5 GO.MF

Fig. Panel plot showing results for the database GO.MF. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

5.8.6 KEGG.pathways

Fig. Panel plot showing results for the database KEGG.pathways. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

6 Exported files

Table. Following files have been exported to the export_files directory.

Table continues below
Description
Raw counts, log CPM and rlog counts
Results of differential expression analysis for all contrasts
Results of gene enrichment analysis with tmod for contrast other_ID0
Results of gene enrichment analysis with tmod for contrast SC2_ID1
Results of gene enrichment analysis with tmod for contrast SC2_vs_other_ID2
File
counts.xlsx
differential_expression_results.xlsx
tmod.other_ID0.xlsx
tmod.SC2_ID1.xlsx
tmod.SC2_vs_other_ID2.xlsx

7 Session Info

## R version 3.6.1 (2019-07-05)
## Platform: x86_64-conda_cos6-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /fast/work/users/jweiner_m/miniconda3/envs/sea_snapNew/lib/libopenblasp-r0.3.6.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] edgeR_3.28.1                limma_3.42.2                writexl_1.3                 orthomapper_0.0.0.9000      enrichplot_1.6.1           
##  [6] tmod_0.46.1                 pander_0.6.3                forcats_0.5.0               stringr_1.4.0               readr_1.3.1                
## [11] tidyr_1.1.0                 tidyverse_1.3.0             glue_1.4.1                  scales_1.1.1                cowplot_1.1.0              
## [16] RColorBrewer_1.1-2          plotly_4.9.2.1              ggplot2_3.3.2               purrr_0.3.4                 tibble_3.0.1               
## [21] dplyr_1.0.0                 magrittr_1.5                DT_0.14                     yaml_2.2.1                  DESeq2_1.26.0              
## [26] SummarizedExperiment_1.16.1 DelayedArray_0.12.3         BiocParallel_1.20.1         matrixStats_0.56.0          Biobase_2.46.0             
## [31] GenomicRanges_1.38.0        GenomeInfoDb_1.22.1         IRanges_2.20.2              S4Vectors_0.24.4            BiocGenerics_0.32.0        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1           backports_1.1.8        fastmatch_1.1-0        Hmisc_4.4-0            plyr_1.8.6             igraph_1.2.5           lazyeval_0.2.2        
##   [8] splines_3.6.1          crosstalk_1.1.0.1      urltools_1.7.3         digest_0.6.25          GOSemSim_2.12.1        plotwidgets_0.4        htmltools_0.5.0       
##  [15] viridis_0.5.1          GO.db_3.10.0           gdata_2.18.0           fansi_0.4.1            checkmate_2.0.0        memoise_1.1.0          cluster_2.1.0         
##  [22] graphlayouts_0.7.0     annotate_1.64.0        modelr_0.1.8           colorDF_0.1.3.9005     prettyunits_1.1.1      jpeg_0.1-8.1           colorspace_1.4-1      
##  [29] ggrepel_0.8.2          blob_1.2.1             rvest_0.3.5            haven_2.3.1            xfun_0.15              tagcloud_0.6           crayon_1.3.4          
##  [36] RCurl_1.98-1.2         jsonlite_1.7.0         hexbin_1.28.1          genefilter_1.68.0      survival_3.2-3         polyclip_1.10-0        gtable_0.3.0          
##  [43] zlibbioc_1.32.0        XVector_0.26.0         DOSE_3.12.0            pheatmap_1.0.12        vsn_3.54.0             DBI_1.1.0              Rcpp_1.0.5            
##  [50] progress_1.2.2         viridisLite_0.3.0      xtable_1.8-4           htmlTable_2.0.0        gridGraphics_0.5-0     europepmc_0.4          foreign_0.8-76        
##  [57] bit_1.1-15.2           preprocessCore_1.48.0  Formula_1.2-3          htmlwidgets_1.5.1      httr_1.4.1             fgsea_1.12.0           gplots_3.0.4          
##  [64] acepack_1.4.1          ellipsis_0.3.1         pkgconfig_2.0.3        XML_3.99-0.3           farver_2.0.3           nnet_7.3-14            dbplyr_1.4.4          
##  [71] locfit_1.5-9.4         reshape2_1.4.4         ggplotify_0.0.5        tidyselect_1.1.0       labeling_0.3           rlang_0.4.7            AnnotationDbi_1.48.0  
##  [78] munsell_0.5.0          cellranger_1.1.0       tools_3.6.1            cli_2.0.2              generics_0.0.2         RSQLite_2.2.0          ggridges_0.5.2        
##  [85] broom_0.5.6            evaluate_0.14          knitr_1.29             bit64_0.9-7            fs_1.4.2               tidygraph_1.2.0        caTools_1.18.0        
##  [92] ggraph_2.0.3           nlme_3.1-148           DO.db_2.9              xml2_1.3.2             compiler_3.6.1         rstudioapi_0.11        beeswarm_0.2.3        
##  [99] png_0.1-7              affyio_1.56.0          reprex_0.3.0           tweenr_1.0.1           geneplotter_1.64.0     stringi_1.4.6          lattice_0.20-41       
## [106] Matrix_1.2-18          vctrs_0.3.1            pillar_1.4.4           lifecycle_0.2.0        BiocManager_1.30.10    triebeard_0.3.0        data.table_1.12.8     
## [113] bitops_1.0-6           qvalue_2.18.0          R6_2.4.1               latticeExtra_0.6-29    affy_1.64.0            KernSmooth_2.23-17     gridExtra_2.3         
## [120] MASS_7.3-51.6          gtools_3.8.2           assertthat_0.2.1       withr_2.2.0            GenomeInfoDbData_1.2.2 hms_0.5.3              ggupset_0.3.0         
## [127] grid_3.6.1             rpart_4.1-15           rmarkdown_2.3          rvcheck_0.1.8          Cairo_1.5-12.2         ggforce_0.3.2          lubridate_1.7.9       
## [134] base64enc_0.1-3